Haemodynamics:Modern applications of basic physiological concepts

Effective haemodynamics require sufficient myocardial contractility, and adequate filling and tonus of the circulatory system. Perioperatively, in the operating theatre, at the postoperative anaesthesia care unit and at the intensive care unit (ICU), patients often need support of their circulatory system. Maintaining stable haemodynamics is challenging and at present our treatments are mostly reactive. Furthermore, selecting the correct treatment modality can be difficult as a proper pathophysiological diagnosis of the cause of the haemodynamic instability might be inapparent.In Part I of this thesis we primarily focus on patients admitted to the ICU and aim to implement a physiological theory on venous return in clinical practice. In Part II we focus on prediction and prevention of haemodynamic instability (defined as intraoperative hypotension) with the use of machine learning

Non-invasive cardiac output monitoring techniques in the ICU

Cardiac output (CO) measurement is an essential part of haemodynamic management in critically ill patients, especially in the intensive care unit (ICU). Since 1970 the ‘clinical reference standard’ for CO monitoring is the use of a pulmonary artery catheter (PAC) for thermodilution (TDPAC). Because of concerns about the safety and overall benefit of the PAC, less invasive and non-invasive techniques have emerged to measure CO in the ICU and these techniques are developing quickly. The aim of this review is to give an overview of the currently available non-invasive techniques for continuous CO monitoring such as oesophageal Doppler, partial carbon dioxide rebreathing, bioimpedance, bioreactance and volume clamping. Furthermore, we evaluate their accuracy in the setting of the ICU by comparing them with the ‘gold standard’ TDPAC. Although the non-invasive techniques show reasonably good results in elective postoperative ICU patients, the noninvasive techniques are not able to replace TDPAC for accurate CO measurements in non-elective ICU patients. Overall the non-invasive techniques seem to perform better as a trend-monitoring device as opposed to measuring absolute CO

The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial

BACKGROUND: Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. The Hypotension Prediction Index (HPI) algorithm is able to predict hypotension minutes before the blood pressure actually decreases. Internal and external validation of this algorithm has shown good sensitivity and specificity. We hypothesize that the use of this algorithm in combination with a personalized treatment protocol will reduce the time weighted average (TWA) in hypotension during surgery spent in hypotension intraoperatively. METHODS/DESIGN: We aim to include 100 adult patients undergoing non-cardiac surgery with an anticipated duration of more than 2 h, necessitating the use of an arterial line, and an intraoperatively targeted mean arterial pressure (MAP) of > 65 mmHg. This study is divided into two parts; in phase A baseline TWA data from 40 patients will be collected prospectively. A device (HemoSphere) with HPI software will be connected but fully covered. Phase B is designed as a single-center, randomized controlled trial were 60 patients will be randomized with computer-generated blocks of four, six or eight, with an allocation ratio of 1:1. In the intervention arm the HemoSphere with HPI will be used to guide treatment; in the control arm the HemoSphere with HPI software will be connected but fully covered. The primary outcome is the TWA in hypotension during surgery. DISCUSSION: The aim of this trial is to explore whether the use of a machine-learning algorithm intraoperatively can result in less hypotension. To test this, the treating anesthesiologist will need to change treatment behavior from reactive to proactive. TRIAL REGISTRATION: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT03376347 . The trial was submitted on 4 November 2017 and accepted for registration on 18 December 2017

Association of intraoperative hypotension with postoperative morbidity and mortality: systematic review and meta-analysis

BACKGROUND: Intraoperative hypotension, with varying definitions in literature, may be associated with postoperative complications. The aim of this meta-analysis was to assess the association of intraoperative hypotension with postoperative morbidity and mortality. METHODS: MEDLINE, Embase and Cochrane databases were searched for studies published between January 1990 and August 2018. The primary endpoints were postoperative overall morbidity and mortality. Secondary endpoints were postoperative cardiac outcomes, acute kidney injury, stroke, delirium, surgical outcomes and combined outcomes. Subgroup analyses, sensitivity analyses and a meta-regression were performed to test the robustness of the results and to explore heterogeneity. RESULTS: The search identified 2931 studies, of which 29 were included in the meta-analysis, consisting of 130 862 patients. Intraoperative hypotension was associated with an increased risk of morbidity (odds ratio (OR) 2.08, 95 per cent confidence interval 1.56 to 2.77) and mortality (OR 1.94, 1.32 to 2.84). In the secondary analyses, intraoperative hypotension was associated with cardiac complications (OR 2.44, 1.52 to 3.93) and acute kidney injury (OR 2.69, 1.31 to 5.55). Overall heterogeneity was high, with an I2 value of 88 per cent. When hypotension severity, outcome severity and study population variables were added to the meta-regression, heterogeneity was reduced to 50 per cent. CONCLUSION: Intraoperative hypotension during non-cardiac surgery is associated with postoperative cardiac and renal morbidity, and mortality. A universally accepted standard definition of hypotension would facilitate further research into this topic

Defining human mean circulatory filling pressure in the intensive care unit

Potentially, mean circulatory filling pressure (Pmcf) could aid hemodynamic management in patients admitted to the intensive care unit (ICU). However, data regarding the normal range for Pmcf do not exist challenging its clinical use. We aimed to define the range for Pmcf for ICU patients and also calculated in what percentage of cases equilibrium between arterial blood pressure (ABP) and central venous pressure (CVP) was reached. In patients in whom no equilibrium was reached, we corrected for arterial-to-venous compliance differences. Finally, we studied the influence of patient characteristics on Pmcf. We hypothesized fluid balance, the use of vasoactive medication, being on mechanical ventilation, and the level of positive endexpiratory pressure would be positively associated with Pmcf. We retrospectively studied a cohort of 311 patients that had cardiac arrest in ICU while having active recording of ABP and CVP 1 min after death. Median Pmcf was 15 mmHg [interquartile range (IQR) 12-18]. ABP and CVP reached an equilibrium state in 52% of the cases. Correction for arterial-to-venous compliances differences resulted in a maximum alteration of 1.3 mmHg in Pmcf. Fluid balance over the last 24 h, the use of vasoactive medication, and being on mechanical ventilation were associated with a higher Pmcf. Median Pmcf was 15 mmHg (IQR 12-18). When ABP remained higher than CVP, correction for arterial-to-venous compliance differences did not result in a clinically relevant alteration of Pmcf. Pmcf was affected by factors known to alter vasomotor tone and effective circulating blood volume.NEW & NOTEWORTHY In a cohort of 311 intensive care unit (ICU) patients, median mean circulatory filling pressure (Pmcf) measured after cardiac arrest was 15 mmHg (interquartile range 12-18). In 48% of cases, arterial blood pressure remained higher than central venous pressure. but correction for arterial-to-venous compliance differences did not result in clinically relevant alterations of Pmcf. Fluid balance, use of vasopressors or inotropes, and being on mechanical ventilation were associated with a higher Pmcf

Coagulation and fibrinolysis in hyperparathyroidism secondary to vitamin D deficiency

Introduction: Abnormal coagulation tests have been observed in patients with primary hyperparathyroidism (HPT) suggesting a prothrombotic effect of parathyroid hormone (PTH). Vitamin D deficiency (VIDD) is the most frequent cause of secondary HPT. Aim of our study was to investigate the influence of HPT secondary to moderate-to-severe VIDD and vitamin D replacement on the coagulation and fibrinolysis system. Subjects and methods: Prospective cohort study of patients with vitamin D <25 nmol/L with and without HPT, and a control group of patients on vitamin D suppletion. At baseline and after 2 months of vitamin D suppletion (900,000 IU in 2 months), endocrine and coagulation markers were measured. Results: 59 patients with VIDD of which 34 had secondary HPT and 36 controls were included. After 2 months of suppletion, vitamin D increased by 399% (VIDD with HPT), 442% (all patients with VIDD) and 6% (controls). PTH decreased by 34% (VIDD with HPT, P < 0.01 for decrease), 32% (all VIDD, P < 0.01) and increased by 8% in the controls (P-values: <0.01 for relative changes between VIDD with HPT or all VIDD patients vs controls). Relative changes in PT, aPTT, fibrinogen, Von Willebrand factor, factors VII, VIII and X, thrombin generation, TAFI, clot-lysis time and d-dimer were not different between patients with VIDD with HPT or all VIDD vs controls. Discussion: Secondary HPT due to VIDD does not have a prothrombotic effect. In contrast with previous reports, PTH does not seem to influence coagulation or fibrinolysis, which is relevant because of the high prevalence of VIDD

Patient blood management in the cardiac surgical setting: An updated overview

In cardiac surgical patients it is a complex challenge to find the ideal balance between anticoagulation and hemostasis. Preoperative anemia and perioperative higher transfusion rates are related to increased morbidity and mortality. Patient blood management (PBM) is an evidence based patient specific individualized protocol used in the perioperative setting in order to reduce perioperative bleeding and transfusion rates and to improve patient outcomes. The three pillars of PBM in cardiac surgery consist of optimization of preoperative erythropoiesis and hemostasis, minimizing blood loss, and improving patient specific physiological reserves. This narrative review focuses on the challenges with special emphasis on PBM in the preoperative phase and intraoperative transfusion management and hemostasis in cardiac surgery patients. It is a “must” that PBM is a collaborative effort between anesthesiologists, surgeons, perfusionists, intensivists and transfusion laboratory teams. This review represents an up to date overview over “PBM in cardiac surgery patients”

Effect of Hypotension Prediction Index-guided intraoperative haemodynamic care on depth and duration of postoperative hypotension: a sub-study of the Hypotension Prediction trial

Background: Intraoperative and postoperative hypotension are associated with morbidity and mortality. The Hypotension Prediction (HYPE) trial showed that the Hypotension Prediction Index (HPI) reduced the depth and duration of intraoperative hypotension (IOH), without excess use of intravenous fluid, vasopressor, and/or inotropic therapies. We hypothesised that intraoperative HPI-guided haemodynamic care would reduce the severity of postoperative hypotension in the PACU. Methods: This was a sub-study of the HYPE study, in which 60 adults undergoing elective noncardiac surgery were allocated randomly to intraoperative HPI-guided or standard haemodynamic care. Blood pressure was measured using a radial intra-arterial catheter, which was connected to a FloTracIQ sensor. Hypotension was defined as MAP <65 mm Hg, and a hypotensive event was defined as MAP <65 mm Hg for at least 1 min. The primary outcome was the time-weighted average (TWA) of postoperative hypotension. Secondary outcomes were absolute incidence, area under threshold for hypotension, and percentage of time spent with MAP <65 mm Hg. Results: Overall, 54/60 (90%) subjects (age 64 (8) yr; 44% female) completed the protocol, owing to failure of the FloTracIQ device in 6/60 (10%) patients. Intraoperative HPI-guided care was used in 28 subjects; 26 subjects were randomised to the control group. Postoperative hypotension occurred in 37/54 (68%) subjects. HPI-guided care did not reduce the median duration (TWA) of postoperative hypotension (adjusted median difference, vs standard of care: 0.118; 95% confidence interval [CI], 0–0.332; P=0.112). HPI-guidance reduced the percentage of time with MAP <65 mm Hg by 4.9% (adjusted median difference: –4.9; 95% CI, –11.7 to –0.01; P=0.046). Conclusions: Intraoperative HPI-guided haemodynamic care did not reduce the TWA of postoperative hypotension

Performance of a Machine Learning Algorithm to Predict Hypotension in Spontaneously Breathing Non-Ventilated Post-Anesthesia and ICU Patients

Background: Hypotension is common in the post-anesthesia care unit (PACU) and intensive care unit (ICU), and is associated with adverse patient outcomes. The Hypotension Prediction Index (HPI) algorithm has been shown to accurately predict hypotension in mechanically ventilated patients in the OR and ICU and to reduce intraoperative hypotension (IOH). Since positive pressure ventilation significantly affects patient hemodynamics, we performed this validation study to examine the performance of the HPI algorithm in a non-ventilated PACU and ICU population. Materials & Methods: The performance of the HPI algorithm was assessed using prospectively collected blood pressure (BP) and HPI data from a PACU and a mixed ICU population. Recordings with sufficient time (≥3 h) spent without mechanical ventilation were selected using data from the electronic medical record. All HPI values were evaluated for sensitivity, specificity, predictive value, and time-to-event, and a receiver operating characteristic (ROC) curve was constructed. Results: BP and HPI data from 282 patients were eligible for analysis, of which 242 (86%) were ICU patients. The mean age (standard deviation) was 63 (13.5) years, and 186 (66%) of the patients were male. Overall, the HPI predicted hypotension accurately, with an area under the ROC curve of 0.94. The most used HPI threshold cutoff in research and clinical use, 85, showed a sensitivity of 1.00, specificity of 0.79, median time-to-event of 160 s [60–380], PPV of 0.85, and NPV of 1.00. Conclusion: The absence of positive pressure ventilation and the influence thereof on patient hemodynamics does not negatively affect the performance of the HPI algorithm in predicting hypotension in the PACU and ICU. Future research should evaluate the feasibility and influence on hypotension and outcomes following HPI implementation in non-ventilated patients at risk of hypotension